中文标题：同时学习基于几何的视觉测程方法的修正和误差模型

中文摘要：本文提出了这样一种观点，即深度学习方法可以用来补充经典的视觉测程管道，以提高它们的精度，并将不确定性模型与它们的估计相关联。我们表明,固有的偏见视觉测距过程可以忠实地学习和补偿,这一个学习架构与联合概率损失函数可以估计一个完整的残余误差的协方差矩阵,定义一个误差模型捕获流程的异方差性。对自动驾驶图像序列的实验评估了同时改进视觉测程的可能性，并估计了与输出相关的误差。

英文标题：Simultaneously Learning Corrections and Error Models for Geometry-based Visual Odometry Methods

英文摘要：This paper fosters the idea that deep learning methods can be used to complement classical visual odometry pipelines to improve their accuracy and to associate uncertainty models to their estimations. We show that the biases inherent to the visual odometry process can be faithfully learned and compensated for, and that a learning architecture associated with a probabilistic loss function can jointly estimate a full covariance matrix of the residual errors, defining an error model capturing the heteroscedasticity of the process. Experiments on autonomous driving image sequences assess the possibility to concurrently improve visual odometry and estimate an error associated with its outputs.

原文作者：Andrea De Maio, Simon Lacroix

原文地址：https://arxiv.org/abs/2007.14943

PDF链接：https://arxiv.org/pdf/2007.14943.pdf